U.S. patent application number 11/096142 was filed with the patent office on 2005-10-20 for noise cancellation in magnetoencephalography and electroencephalography with isolated reference sensors.
Invention is credited to Espy, Michelle A., Kraus, Robert H. JR., Matlachov, Andrei, Volegov, Petr.
Application Number | 20050234329 11/096142 |
Document ID | / |
Family ID | 35097176 |
Filed Date | 2005-10-20 |
United States Patent
Application |
20050234329 |
Kind Code |
A1 |
Kraus, Robert H. JR. ; et
al. |
October 20, 2005 |
Noise cancellation in magnetoencephalography and
electroencephalography with isolated reference sensors
Abstract
An apparatus measures electromagnetic signals from a weak signal
source. A plurality of primary sensors is placed in functional
proximity to the weak signal source with an electromagnetic field
isolation surface arranged adjacent the primary sensors and between
the weak signal source and sources of ambient noise. A plurality of
reference sensors is placed adjacent the electromagnetic field
isolation surface and arranged between the electromagnetic
isolation surface and sources of ambient noise.
Inventors: |
Kraus, Robert H. JR.; (Los
Alamos, NM) ; Espy, Michelle A.; (Los Alamos, NM)
; Matlachov, Andrei; (Los Alamos, NM) ; Volegov,
Petr; (Albuquerque, NM) |
Correspondence
Address: |
UNIVERSITY OF CALIFORNIA
LOS ALAMOS NATIONAL LABORATORY
P.O. BOX 1663, MS A187
LOS ALAMOS
NM
87545
US
|
Family ID: |
35097176 |
Appl. No.: |
11/096142 |
Filed: |
March 31, 2005 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
60562337 |
Apr 15, 2004 |
|
|
|
Current U.S.
Class: |
600/409 |
Current CPC
Class: |
A61B 5/6814 20130101;
A61B 5/246 20210101; A61B 5/30 20210101; A61B 5/377 20210101 |
Class at
Publication: |
600/409 |
International
Class: |
A61B 005/05; A61B
005/04 |
Goverment Interests
[0002] This invention was made with government support under
Contract No. W-7405-ENG-36 awarded by the U.S. Department of
Energy. The government has certain rights in the invention.
Claims
What is claimed is:
1. Apparatus for measuring electromagnetic signals from a weak
signal source, comprising: a plurality of primary sensors in
functional proximity to the weak signal source; an electromagnetic
field isolation surface arranged in proximity to the primary
sensors and between the weak signal source and sources of ambient
noise; and a plurality of reference sensors in proximity to the
electromagnetic field isolation surface and arranged between the
electromagnetic isolation surface and sources of ambient noise.
2. The apparatus of claim 1, wherein the electromagnetic field
isolation surface is a superconducting surface.
3. The apparatus of claim 1, wherein the reference sensors are
superconducting quantum interference devices (SQUIDs).
4. The apparatus of claim 1, wherein the primary sensors are
SQUIDs.
5. The apparatus of claim 1, wherein the primary sensors are
electropotential sensors.
6. A method for obtaining a noise-free signal from a weak source of
electromagnetic fields comprising: placing primary sensors on a
first side side of an electromagnetic field isolation surface and
reference sensors adjacent on a second side of the electromagnetic
field isolation surface opposing the first side; placing the
primary sensors in operative proximity to the weak source of
electromagnetic field; forming a source signal from an output of
the primary sensors that includes a signal from the weak source of
electromagnetic fields; forming a reference signal from an output
of the reference sensors comprised essentially of ambient
electromagnetic noise; forming a correcting signal from the
reference signal; subtracting the correcting signal from the source
signal to output the noise-free signal.
7. The method of claim 6, wherein the weak source of
electromagnetic fields is a human brain.
8. The method of claim 7, wherein the weak source of
electromagnetic fields is electrical currents generated by the flow
of ions in and around active brain neurons.
9. The method of claim 6 wherein the electromagnetic field
isolation surface is a superconducting imaging surface.
Description
RELATED CASES
[0001] This application claims the benefit of provisional
application Ser. No. 60/562,337, filed Apr. 15, 2004.
FIELD OF THE INVENTION
[0003] The present invention relates generally to
magnetoencephalography (MEG) and electroencephalography (EEG), and,
more particularly, to noise cancellation in MEG and EEG
recordings.
BACKGROUND OF THE INVENTION
[0004] Magnetoencephalography (MEG) is a noninvasive technique that
measures the direct consequence of neuronal activity in the living
brain. MEG measures magnetic fields that emanate from the head,
unimpeded by anatomy, and by the electromagnetic "right-hand rule"
are associated with electrical currents generated by the flow of
ions in and around active neurons. Other functional brain imaging
modalities such as fMRI, PET, and optical techniques infer brain
function indirectly by measuring changes in blood flow, volume,
oxygenation, etc. that are hypothesized to be associated with
neuronal activity. MEG is further unique in that it, together with
EEG (electroencephalography), is the only noninvasive method of
measuring brain function at sub-millisecond temporal resolution or
better. The physiological processes underlying the indirect
measures of brain function inherently evolve at a far slower
timescale.
[0005] Cohen primary reported detecting a magnetic signal
originating from the human brain in 1968 using a
non-superconducting sensor [1]. Shortly thereafter a RF (radio
frequency) SQUID (Superconducting QUantum Interference Device)
sensor was used for the first time to measure biomagnetic signals
originating from the human heart [2], and the human brain [3]. The
evoked-response brain activity measured with a SQUID sensor was
reported by Brenner, et al., in 1975 [4]. Once it was clear that
human brain activity could be observed and measured non-invasively
by instruments consisting of one or a few sensors, the goal quickly
shifted to precisely localizing the neuronal sources responsible
for the measured magnetic activity. The position and vector
characteristics of these neuronal sources can be estimated from the
inverse solution of the field distribution measured outside the
head.
[0006] Magnetic field distributions were originally obtained using
single sensor systems that mapped fields around the head by moving
the sensor in space and presenting the same stimulus to the subject
at each sensor location. By the mid-1990's multiple sensors arrays
covering much of the head were commercially available from
companies including 4D-Neuroimaging in the US, CTF in Canada, and
Neuromag in Finland, with large projects also in place in Japan.
These systems consisted of 100 sensors or more spaced at various
intervals over the head using various configurations. Sensor
arrangement variables including gradiometric configurations, sensor
spacing and other parameters have been quantitatively modeled by
Flynn [5] and Mosher [6] for performance optimization. Different
designs are driven by various design parameters impacting detection
efficiency, cost, and target application.
[0007] Although MEG is not truly tomographic, and source
reconstruction is limited by solutions of the electromagnetic
inverse problem, constraints used for source localization have
demonstrated reliable and accurate results. Current MEG
instrumental source location accuracy reported in the literature is
approximately 2 to 4 mm, depending on signal-to-noise (S/N) of the
measured MEG signal, source parameters, and a variety of other
parameters. Typically, 5 to 10 mm accuracy is attained in most
medical applications with the latest instruments. In addition to
locating the sources of neuronal activity, MEG temporal resolution
is unsurpassed by any other brain imaging method.
[0008] A typical MEG experiment involves numerous successive
presentations of a given stimulus or group of stimuli to a subject
for which the evoked brain response is measured. An exemplary
protocol would be stimulating the receptor nerves on a fingertip
using a pneumatic button. The activated touch receptors transmit a
signal via peripheral afferents and the spinal cord to the
thalamus, which in turn transmits the signal via third-order fibers
to activate a small portion of the sensory region of cerebral
cortex (primary somatosensory cortex). Neurons in the cortex are
arranged in columnar structures that are associated with the
receptive field location and the type of stimulus.
[0009] As neurons are activated, positive ions are rapidly
transported through the cell membrane into the cell
(depolarization) followed by a slightly slower outflow of positive
ions (repolarization). The entire process lasts approximately 3
milliseconds (msec), not including the afferent signal propagation.
This phenomenon propagates along some portion of a neuron
generating a concentrated ionic flow or intracellular current
inside the neuron.
[0010] MEG measures the magnetic fields that are associated through
Maxwell's equations with movement of charge. Although electrodes
implanted in cortex near or inside of cell bodies can measure the
response of individual neurons, it is impossible for current
technology to observe such a signal noninvasively (i.e., from
outside the skull). MEG is a viable technique because all of the
neurons in a given cortical column, or set of columns, respond
roughly synchronously to a single stimulus, resulting in a
superposition of the fields from hundreds or thousands of activated
neurons. Nonetheless, the typical MEG signal measured during evoked
response experiments is very tiny, only tens to hundreds of
femtotesla (10.sup.-14 to 10.sup.-13T). MEG instruments today
universally use SQUID sensors to measure these extraordinarily
small magnetic fields. A state-of-the-art production SQUID will
have sensitivities down to .about.2 fT/{square root}Hz (one
femtotesla, fT, is 10.sup.-15T).
[0011] The greatest challenge to measuring magnetic fields
resulting from brain activity and localizing neuronal sources is
the massive amount of magnetic noise in our environment. Magnetic
noise in an urban setting is commonly in the range of 10.sup.-9 to
10.sup.-5T generated by AC line noise, automobiles, trains,
elevators, etc. Urban magnetic noise is many orders of magnitude
larger than the fields produced by the brain. A variety of
techniques have been used to reduce the ambient magnetic fields for
MEG measurements including: 1) locating the MEG instrument and
subject in a magnetically shielded room (MSR), 2) using
gradiometric magnetic field sensors coupled to SQUIDs, 3) active
compensation of magnetic noise with field coils, 4) measuring or
estimating the ambient noise fields and digitally subtracting the
noise from sensors that are measuring brain activity, and 5)
averaging MEG data from numerous stimuli. In addition to these
hardware-based approaches, a broad array of additional
post-processing software algorithms have been developed to reduce
noise in MEG data, such as simple filters (low-pass, high-pass,
band-stop, etc.), ICA [7, 8], etc.
[0012] A typical two-layer MSR will reduce noise by at least 30 dB
at 0.1 Hz and 80 dB at 100 Hz for a cost of several hundred
thousand US dollars. Gradiometers will further reduce noise at the
cost of some reduction of brain signal. Gradiometer performance
depends on numerous parameters including gradiometer design (e.g.,
gradiometer order and baseline), quality of fabrication, and
characteristics of the noise source (e.g., physical size,
proximity). Typical primary-order gradiometers reduce ambient
magnetic fields from sources such as AC power lines by about 40
dB.
[0013] Active noise compensation involves measuring the noise field
and using sets of large magnetic field coils relatively distant
from the MEG system to cancel the noise field by superposition [9].
Such systems produce a limited set of spatial and temporal
frequencies (due to the magnetic field coil size and inductance),
are extremely sensitive to mechanical effects such as vibration,
and are costly. A second form of active field cancellation uses the
modulation and feedback coil common to most SQUID sensors to
generate the noise compensation field at the SQUID pickup loop
[10]. This method has greater frequency response (though still
limited by processing and conversion speeds), fewer mechanical
issues, and is less costly. This approach enables suppressing
ambient fields with higher spatial frequencies because the
background fields can be measured in close proximity to the MEG
sensors and the field compensation is performed directly at the MEG
sensors. The drawback is that the ambient field sensors placed
close to the MEG array will be sensitive to sources in the brain;
consequently, some brain signal will be cancelled along with the
background. Although active noise cancellation techniques have the
advantage of reducing the dynamic range of the MEG signals
(typically dominated by ambient noise), any noise inherent in the
background field measurement and generation of cancellation fields
will be irreversibly added to the MEG signals.
[0014] Reference sensors are used by most modern MEG systems to
further reduce background by measuring the ambient fields near the
MEG sensor array, projecting the ambient field to the MEG sensors,
and digitally subtracting the ambient field contribution to the
signal measured by the MEG sensors [11, 12]. Large dynamic range
analog and digital electronics are required for both background and
MEG signals to avoid saturation. Such large dynamic range
electronics have only become commercially available and affordable
in recent years. Additionally, reference sensors must measure the
background field with low noise and sufficient resolution to
minimize increasing the noise floor of the MEG signals.
[0015] Simultaneous sampling of ambient and MEG fields with
subsequent projection and subtraction of background from brain
signals effectively removes noise up to the Nyquist limit and
eliminates phase lag caused by real-time conversion, processing,
and feedback inherent in all active noise cancellation approaches.
In order to accurately characterize the ambient field at the
primary MEG sensors, the reference sensors must be placed as close
as possible to the MEG array. However, as the reference sensors
approach the MEG array, they will be increasingly sensitive to
magnetic fields originating in the brain. Any method that projects
fields measured at the reference sensors to the primary MEG sensors
and subtracts the projected values will necessarily subtract some
portion of the brain signal contained in the reference sensor
measurement. Consequently, there is a tradeoff between placing
reference sensors as close to the MEG sensors as possible (to
accurately characterize the spatial frequency of ambient fields)
while minimizing the brain signal contained in the reference sensor
measurement.
[0016] Other techniques have been developed using reference sensors
in close proximity to the primary MEG sensors or using the MEG
sensors themselves to measure the ambient background fields. These
techniques invariably apply a model-based description of the noise
to separate the ambient field contribution from the fields
originating in the brain. Although this approach has been shown to
be quite effective, it suffers from the imperfect model describing
the noise and consequently does not completely remove ambient field
noise from the MEG signal. It may also remove some signal
originating from the brain that is not entirely orthogonal to the
model description of the ambient noise sources.
[0017] Finally, averaging MEG data from multiple stimuli (data
epochs) is commonly used to increase MEG signal-to-noise (S/N).
Every presentation of the stimulus evokes a reproducible and nearly
identical cortical response; consequently the S/N of evoked
response measurements can be greatly improved by averaging the data
from a large number of stimuli. In theory, this averaging reduces
both ambient field noise and brain activity that is uncorrelated
with the stimuli. Although the noise is reduced by roughly
1/{square root}N.sub.e (where N.sub.e is the number of epochs
recorded and averaged), the evoked response signal begins to
degrade for large numbers of stimuli. Consequently S/N does not
increase indefinitely. Furthermore, averaging epochs is not
possible for all functional brain studies (e.g. epilepsy) and there
is increasing evidence that effects such as alterations in the
dynamics of ongoing neural synchrony would be washed out by
averaging multiple epochs of evoked response recordings.
[0018] Various objects, advantages and novel features of the
invention will be set forth in part in the description which
follows, and in part will become apparent to those skilled in the
art upon examination of the following or may be learned by practice
of the invention. The objects and advantages of the invention may
be realized and attained by means of the instrumentalities and
combinations particularly pointed out in the appended claims.
SUMMARY OF THE INVENTION
[0019] In accordance with the purposes of the present invention, as
embodied and broadly described herein, one aspect of the present
invention includes apparatus for measuring electromagnetic signals
from weak signal sources. A plurality of primary sensors is placed
in functional proximity to the weak signal sources with an
electromagnetic field isolation surface arranged in proximity to
the primary sensors and between the weak signal sources and sources
of ambient noise. A plurality of reference sensors is placed in
proximity to the electromagnetic field isolation surface opposite
the primary sensors and on the same side of the electromagnetic
isolation surface as the sources of ambient noise.
[0020] Another aspect of the present invention includes a method
for obtaining a noise-free signal from weak sources of
electromagnetic fields. Primary sensors are placed on a first side
of an electromagnetic field isolation surface and reference sensors
are placed on a second side of the electromagnetic field isolation
surface opposing the primary side. The primary sensors are placed
in operative proximity to the weak source of electromagnetic field.
A source signal is formed from an output of the primary sensors
that includes a signal from the weak source of electromagnetic
fields and a reference signal is formed from an output of the
reference sensors comprised essentially of ambient electromagnetic
noise. A correcting signal is then formed from the reference signal
and subtracted from the source signal to output the noise-free
signal.
BRIEF DESCRIPTION OF THE DRAWINGS
[0021] The accompanying drawings, which are incorporated in and
form a part of the specification, illustrate the present invention
and, together with the description, serve to explain the principles
of the invention. In the drawings:
[0022] FIG. 1 is a pictorial illustration of a SIS-MEG array
showing the helmet-shaped SIS surrounded by a cylindrical support
and primary sensor array and exemplary reference sensors.
[0023] FIG. 2 graphically depicts magnetic field noise measured
both inside and outside the array shown in FIG. 1.
[0024] FIGS. 3A and 3B graphically depict measured adaptive noise
cancellation (ANC) performance without a subject in the array shown
in FIG. 1, where FIG. 3A is for a SQUID near the brim of the helmet
and FIG. 3B is for a SQUID near the apex of the helmet.
[0025] FIGS. 4A and 4B graphically depict measured ANC performance
with a subject seated in the array shown in FIG. 1, where FIG. 4A
is for a SQUID near the brim of the helmet and FIG. 4B is for a
SQUID near the apex of the helmet.
[0026] FIG. 5 graphically compares the de-noising effectiveness for
the ANC method with a notch filter designed for 60 Hz and
harmonics.
DETAILED DESCRIPTION
[0027] A unique whole-head MEG system incorporating a
superconducting imaging-surface (SIS) is provided with the goal of
dramatically improving S/N and source localization accuracy while
mitigating the extraordinary cost of current systems, as described
in U.S. Pat. No. 5,008,622, issued Apr. 6, 1991, and incorporated
herein by reference. A prior art Los Alamos SIS-MEG system [13, 14]
consists of an array of 149 low-T.sub.c SQUID magnetometers
surrounded by a superconducting helmet-like structure (fabricated
with lead, a type-I superconductor below .about.8K).
[0028] In addition to noise-free recording of functional brain
signals with MEG, the apparatus and method of the present invention
have further application to electroencephalography (EEG) recordings
using SQUID sensors to measure the ambient background fields and
electropotential sensors to measure brain signals. Brain function
signals recorded simultaneously by MEG and EEG arrays are becoming
increasingly more common because the information content in such
recordings is far greater than is either alone, or even both
combined but acquired separately.
[0029] While EEG is typically far less susceptible to noise than
MEG, ambient environmental noise is increasingly becoming a
hindrance to EEG recordings. For example, EGI (Electrical
Geodesics, Inc.) has developed extremely high-density EEG electrode
arrays that do not require the scalp to be "pricked" by an
electrode. The contact is made directly by a conductive salt
solution contained in the electrodes. While this greatly
facilitates the ease of administering the dense array EEG to the
subject, the electrical connection to the subject head is more
resistive; consequently it is more susceptible to noise pickup.
Regardless of cause, "ambient environmental" noise picked up by EEG
sensors is defined as originating from electromagnetic sources
outside the subject.
[0030] As used herein, the term "electromagnetic" field includes
magnetic and/or electric fields arising from the brain that are
measured separately or together.
[0031] The SIS system is based on the principal that Meissner
currents flow in the surface of superconductors, preventing any
significant penetration of magnetic fields. The original impetus
behind the SIS-MEG system was to use a hemispherical SIS with a
brim, or helmet, surrounding the SQUID sensor array to shield the
SQUIDs from sources outside the helmet while they measure fields
from nearby sources within the helmet. Sensors inside the helmet
are shielded from sources outside the helmet (ambient magnetic
noise) to varying degrees depending on the sensor location relative
to the lower edge (brim) of the helmet. The effectiveness varies
from >25 dB for sensors near the SIS brim to .about.60 dB for
sensors well above the brim. Unlike magnetic shielding by other
non-superconducting methods (for example, MSR's) whose
effectiveness drops off rapidly at low frequencies, the SIS
shielding is independent of frequency.
[0032] While the original application of the SIS was to shield the
SQUIDs that sense brain activity, the present invention recognizes
that the SIS can be used to substantially shield reference sensors
located immediately outside the helmet from brain sources, i.e.,
provides an isolating surface. This enables precise measurement of
ambient background fields in very close proximity to the brain
(primary) sensors without any contribution of signal from the
sources in the brain. The SIS has the additional advantages of
providing frequency independent shielding, being completely
passive, and low-cost. While the SIS is a preferred way of
providing an isolating surface, the use of the term "isolating
surface" includes any similar device that prevents any significant
penetration of magnetic fields through the surface to the reference
sensors.
[0033] Localizing sources of neuronal activation from MEG
measurements requires a complete description of the "forward
physics" that describes how neuronal currents lead to magnetic
fields at the SQUID sensors. The MEG forward model includes the
complex neuronal source model that incorporates intracellular ionic
currents, intercellular and extracellular volume currents, brain
structure, and conductivities. The forward model for the SIS-MEG
system further includes the effect of the superconducting surface
on the fields generated by the primary sources. A finite element
model (FEM) description of the SIS with the exact as-built geometry
is used to accurately describe how the SIS impacts the forward
physics of source models [15]. The FEM is used to calculate the
distribution of Meissner currents in the complicated surface
geometry of the SIS such that B.sub..perp.=O at the surface.
[0034] In accordance with the present invention, an additional
benefit of an isolating surface is realized in conjunction with the
use of reference sensors to measure the ambient magnetic fields and
subtract the noise contribution to the primary MEG sensors. The SIS
is used as an exemplary isolating surface here, but the use of the
term "SIS" is not intended to be limiting with respect to the
formation of an isolating surface.
[0035] The SIS-MEG system design, unlike any prior approach,
enables the use of reference sensors to measure background fields
in proximity to the isolation surface (e.g., within a few
centimeters of the SIS) in close proximity to the primary sensors
(e.g., within a distance that is close relative to the separation
from all noise sources, typically a few centimeters) while having
no measurable sensitivity to sources inside the SIS helmet. A
consequence of the small spatial extent of brain sources and
containment within the SIS is that reference sensors are
substantially shielded (>10.sup.4 reduction or >80 dB) from
sources in the brain. In practice, any residual brain signal
outside the SIS is well below the noise threshold of the SQUID
magnetometers. The complete insensitivity of the SIS-MEG system
reference sensors to brain sources is the key that enabled
development of an adaptive noise cancellation (ANC) approach
without concern about reducing the signal of interest or making any
assumptions about the spatial character of the noise sources. An
extremely important benefit is that no information about is
reference sensor position is required for this approach. In
addition, no model-based description of the noise or brain sources
is needed to separate the contributions of the sources to the
measured magnetic fields.
[0036] FIG. 1 pictorially depicts a SIS-MEG system according to one
embodiment of the present invention. All elements of the system
except for the reference sensors 3,4 for measuring ambient magnetic
fields are described in the '622 patent and are only briefly
described in relation to reference sensors 3,4. Shell 2, shown in
FIG. 1, may have one or more superconducting cylindrical extensions
as depicted in the '622 patent, supra, attached so that weak
sources inside the shell are completely isolated from primary
sensors 1 used to measure the weak magnetic fields from the sources
inside the shell Typical cryogenic dewar construction 5 is used to
isolate the cryogenic shell and sensors from sources at room
temperature. Superinsulation commonly used in cryogenic dewar
construction, consisting of multiple layers of a highly reflective
metal (such as aluminum, silver, or gold) evaporated on mylar, also
serves as an RF shield to isolate sensors 1 from ambient RF noise.
A vacuum is maintained between the inner and outer surfaces of the
cryogenic dewar, as is common for such devices.
[0037] As constructed here, the superconducting shell 2 and all
magnetic field sensors 1, 3, and 4 are immersed in a liquid cryogen
bath 6 (such as liquid helium or liquid nitrogen) that is contained
within dewar 5. Cooling shell 2 and sensors 1, 3, and 4 are
required to reach the operational characteristics (superconducting
state) of these type of devices.
[0038] While the approach described herein applies to any set of
primary and reference magnetic field sensors, superconducting
quantum interference devices, SQUIDs, as sensors 1, 3, and 4, and a
lead SIS surface as shell 2 are exemplary embodiments. As shown,
each primary sensor 1 used to measure the weak magnetic field from
sources of interest, and reference sensors 3, 4 used to measure the
ambient magnetic field noise, 3, are superconducting pickup loops
directly coupled to DC SQUIDs. An array of integrated SQUID primary
sensors 1, e.g., 149 sensors, are spaced around the inside of the
SIS in order to measure signals being generated by all regions of
the brain.
[0039] In accordance with the present invention, reference sensors
3, 4 are located about hemispherical shell 2 to detect ambient
electromagnetic noise from which a correction signal is derived for
the outputs of the primary sensors 1, used to measure the weak
fields from the sources of interest. A suitable reference sensor 3,
4 is similar to that used for detecting the weak fields of
interest, e.g., SQUIDs. In an operating instrument, the same sensor
design used to measure fields from sources of interest was used for
reference sensors. However, any sensor with sufficient field
sensitivity to measure magnetic field noise to the threshold of the
noise floor required could be used. Examples of other possible
sensors of this type include high-sensitivity magnetoresistive
(GMR) type sensors, and the recently reported atomic magnetic
sensors.
[0040] The shielding effect of the SIS of brain sources from
reference sensors was modeled using the SIS forward physics model
described in ref [13]. The shielding of brain sources from
reference sensors was >85 dB for any source in a typical brain
region within the SIS. This was confirmed by phantom source
measurements using a precision 65-coil MEG phantom, where a lower
limit of the shielding for 3 mm sources was measured to be >80
dB (frequency independent).
[0041] The configuration of reference sensors was chosen after
modeling a broad array of background sources and optimizing the
reference sensor positions relative to the MEG primary sensor
array. It was discovered that any group of reference sensors placed
with a variety of orientations in close proximity to the primary
sensors that measure signals from the sources of interest would
perform very well. It was also found that one could improve the
performance of the noise rejection algorithm if reference sensors
were able to measure the vector character of the ambient field.
While noise cancellation was improved with increasing number of
reference sensors, the residual noise rapidly approached the
intrinsic asymptote of the sensors measuring signals from the
sources of interest. Thus, the reference sensors used to
demonstrate this technique were attached in groups of three sensors
3, 4 to three orthogonal sides of a cube (not shown). The three
sensors 3, 4 on each cube sampled the three orthogonal components
of the magnetic field at a given location. Five cubes (fifteen
reference sensors total) were spaced around the SIS helmet: one
near the apex and four spaced at approximately equal intervals
around the helmet at approximately 30-degrees above the brim.
[0042] The ANC algorithm multiplies reference sensor signals by a
mixing matrix and subtracts the resultant values from the MEG
sensor signal. The mixing matrix is derived from the reference
sensor measurement of ambient fields to determine the relative
weighting of each reference sensor in computing the ambient field
at each primary MEG sensor. The MEG and reference signals are
defined as:
{s.sub.i(t)}.sub.1.sup.n--MEG channels Eqn. 1
{q.sub.i(t)}.sub.1.sup.m--reference channels Eqn. 2
[0043] Where n is the number of primary signal sensors and m is the
number of reference sensors.
[0044] The corrected signal, {tilde over (s)}.sub.i, is calculated
for each channel by multiplying the reference channel signals by a
mixing matrix and subtracting the product from the raw MEG signal,
s.sub.i where "i" represents the `ith` channel. This can be written
in matrix notation or explicitly as: 1 s ~ = s - A q Eqn . 3 s [ s
i ( t ) ] T Eqn . 4 q [ q i ( t ) ] T Eqn . 5 A [ a i , j ] Eqn . 6
s ~ i ( t ) = s i ( t ) - j = 1 m a i , j q j ( t ) i = 1 n Eqn .
7
[0045] The mixing matrix, A, is computed by least square
minimization of the residual in the signal after subtracting the
background, which is determined by multiplying the signal-reference
sensor correlation matrix by the inverse of the reference-reference
correlation matrix, C.sub.sq and C.sub.qq.sup.-1 respectively. This
simple approach can be applied to the SIS-MEG system because no
brain signal is present in the reference sensors. This can be
described in matrix notation by:
A:min J {A,s,q} Eqn. 8
J=.parallel.{tilde over (s)}.sub.i(t).parallel..sub.t.sub..sub.2
A=C.sub.sq.multidot.C.sub.qq.sup.-1 Eqn. 9
C.sub.sq=[<s.sub.i.vertline.q.sub.k>],
C.sub.qq=[<q.sub.k.vertlin- e.q.sub.j>] Eqn. 10
<a.vertline.b>=.intg.a(t)b(t)dt Eqn. 11
[0046] Because sources within the SIS helmet are extraordinarily
well shielded from the reference sensors, A can be computed at any
time during the measurement of MEG signals. Investigations have
shown that A changes only modestly over the course of a protocol,
typically a few tens of minutes. Finally, it should be noted that
adaptive noise cancellation approach requires no information about
the position of the reference sensors.
[0047] Results:
[0048] The effectiveness of the ANC method was tested in a variety
of experiments with and without subjects. The SIS-MEG system was
located in a single-layer MSR consisting of an aluminum eddy
current shell and mu-metal static field shield. The room measured
2.7 m.times.2.5 m.times.2.5 m with a full-height door at one
corner. The SIS-MEG system was located approximately in the center
of the room. A plot of the ambient magnetic fields measured inside
and out of the MSR is shown in FIG. 3. The effective shielding of
the MSR at 60 Hz is .about.40 dB. The significant noise increase at
frequencies below 30 Hz inside the MSR is caused by mechanical
vibrations coupling to the MSR and MEG system. This noise is
dramatically reduced when building ventilation is turned off. It
should be noted that the ambient magnetic noise inside the single
layer MSR is similar to that measured in many unshielded
environments where MEG systems are located.
[0049] The effectiveness of the ANC method implemented for the
SIS-MEG system was demonstrated by comparing the raw and corrected
signals for SQUID sensors near the brim and apex of the SIS helmet
for cases with and without a subject in the MEG array. All SQUID
channels were sampled simultaneously at 3,000 samples per second
with 24-bit ADCs using 1.2 kHz anti-aliasing filters. As noted
above, the passive shielding of the SIS for primary SQUID sensors
near the apex of the SIS was far more effective than for those near
the brim, clearly seen in FIGS. 3A and 3B, where noise in a brim
SQUID is more than an order of magnitude larger than in an apical
SQUID. After ANC, the de-noised SQUID signal is very similar for
both brim and apical SQUID channels. This demonstrates the power of
the ANC algorithm to de-noise sensor signals contaminated by noise
ranging over several orders of magnitude.
[0050] Furthermore, the ANC method herein approaches the optimum
de-noising attainable since the residual noise is very near the
intrinsic SQUID noise of 2-3 fT/{square root}Hz for the SQUID near
the apex and only slightly higher for the brim SQUID. Only a 1 kHz
anti-aliasing filter has been applied to the data shown in FIGS. 3A
and 3B where 0-130 Hz of the full 0-1 kHz measured spectra is
plotted to more clearly illustrate the effectiveness of the ANC
method in the frequency range typically of interest for MEG
measurements. The complex noise spectra below 30 Hz in both brim
and apical raw data is largely due to the building ventilation
unit, located approximately 10 m away, mechanically coupling to the
MEG system through floor vibrations. The fact that low frequency
noise is not cancelled as effectively at the brim as the apex is
probably due to insufficient rigidity of the SQUID-SIS structure.
The noise spectra for all apical SQUID magnetometers are
indistinguishable from inherent noise spectra measured for these
devices.
[0051] FIGS. 4A and 4B show the results of the ANC method with a
subject seated under the MEG dewar immediately prior to executing
an evoked response protocol. Data were acquired as described above.
The full spectral results are plotted on a log scale to illustrate
the effectiveness of the ANC over the full magnitude and frequency
range of the signal. The ANC de-noising is as effective as without
a subject and the residual noise in the apical channels is at or
near the intrinsic SQUID noise and slightly larger for the brim
channels. The pronounced knee at .about.50 Hz in both the brim and
apical de-noised spectra is caused by the uncorrelated brain
activity which is not removed by the ANC.
[0052] The huge contribution of 60 Hz line noise to the uncorrected
MEG recordings could lead one to speculate that a well-crafted
notch filter can effectively remove most of the noise in the
spectra. A notch filter was used to remove all harmonics of 60 Hz
with an absolute minimum of added artifact. A comparison of the
effectiveness of applying this notch filter to the same data as
shown in FIGS. 3A and 3B, with the ANC method is shown in FIG. 5.
While the notch filter reduced line noise by .about.50 dB, the ANC
method reduced the 60 Hz line noise by nearly 70 dB. Furthermore,
while the notch filter removes signals independent of any
correlation with noise or brain signals, the ANC method does not
remove any signal that was uncorrelated with the reference sensors.
Finally, the ANC reduced noise across all frequencies upon which
the notch filter had no effect. This broad-band noise is due to
Johnson noise in metallic components near the SQUID array and seen
primarily in the brim channels of the MEG channels.
[0053] Table 1 summarizes the effectiveness of the ANC method for
various frequency bands from near DC to 150 Hz. The data in Table 1
is for the SIS-MEG system without a subject present. Although most
ambient field cancellation approaches cite the "noise reduction
factor" as a measure of effectiveness, the ultimate performance is
for the measured noise to be that of the intrinsic SQUID noise. In
fact, we have demonstrated that the ANC method herein removes
substantially all ambient field contribution to the signal measured
by the MEG SQUIDs. The residual noise observed in apical SQUID
sensors after applying ANC is at the SQUID noise floor above 15 Hz
and within a factor of two of the SQUID noise floor at lower
frequencies. The residual noise in brim channels is typically a
factor of two to three greater than the SQUID noise floor at higher
frequencies and a factor of five for lower frequencies. In general,
the largest noise reduction factors were realized where the noise
is greatest, specifically at 60 Hz (power line frequency) and
harmonics. Further, the largest noise in any frequency band is
observed for the brim channels. We have demonstrated noise
reduction factors for our ANC in excess of 10.sup.4 (>80 dB).
The noise reduction factor appears to be limited by the magnitude
of the noise available to remove and the noise floor of the
SQUID.
1TABLE 1 Realized noise cancellation performance for an apical MEG
channel (#1) with a brim channel (#65) PSD for ch#1 Noise PSD for
ch#65 Noise Typical Frequency (fT/Hz) reduction (fT/Hz) reduction
SQUID Range, Hz raw corrected factor, ch#1 raw corrected factor,
ch#65 noise (fT/{square root}Hz) 2-5 11.8 6.4 2 126.6 26.7 5 3-4
15-25 18.2 3.3 6 787.5 12.3 64 2-3 59.5-60.5 4057.7 2.5 1600
73264.4 7.1 >10000 2-3 90-100 1.8 1.4 1 27.7 4.9 5 <2 130-15-
1.7 1.4 1 26.8 4.5 5 <2
[0054] The slightly higher residual noise observed in the brim
SQUID sensors may be attributed to at least two causes. Primary, as
noted above, the brim of the SIS structure appears to be less rigid
than the apex; consequently, the observed vibration couples
differently to the SQUIDs located in this region than to the
reference sensors. This effect prevents optimal correlation between
reference and MEG sensor measurements of the ambient field and
reduces the effectiveness of the method. The fact that the
reference sensors are located closer to the apex of the SIS
(further from the brim) may be a second contributing factor to the
less optimal performance in de-noising for the brim sensors.
[0055] ANC has proven to be exceptionally effective in all of the
MEG protocols used to date. Exceptionally noise-free evoked
response MEG signals were measured in an environment with 10-100 nT
ambient field noise (primarily from 60 Hz power line sources). This
is consistent with many unshielded environments. Although the SIS
provides moderate shielding of MEG SQUID magnetometers, the most
significant effect of the SIS is to shield reference sensors from
the brain sources.
[0056] The application of the MEG apparatus and method described to
EEG is straightforward. The reference sensors are able to measure
ambient environmental noise while being completely insensitive to
the brain signal. Thus, during simultaneous MEG-EEG recordings, the
noise data acquired by SQUID reference sensors, as described above,
can be used to remove all ambient environmental noise from the EEG
sensors. This is a consequence of the complementarity of electric
and magnetic parts of the electromagnetic field. Noise rejection is
accomplished using the above algorithms, except that a phase term
is added to either equation 1 or 2 as required by the inherent
difference in character between magnetic and electric signals.
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[0072] The foregoing description of the invention has been
presented for purposes of illustration and description and is not
intended to be exhaustive or to limit the invention to the precise
form disclosed, and obviously many modifications and variations are
possible in light of the above teaching. The embodiments were
chosen and described in order to best explain the principles of the
invention and its practical application to thereby enable others
skilled in the art to best utilize the invention in various
embodiments and with various modifications as are suited to the
particular use contemplated. It is intended that the scope of the
invention be defined by the claims appended hereto.
* * * * *